scholarly journals Determining the Image Base of ARM Firmware by Matching Function Addresses

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruijin Zhu ◽  
Baofeng Zhang ◽  
Yu-an Tan ◽  
Yueliang Wan ◽  
Jinmiao Wang
Keyword(s):  
Experimental Results ◽  
Matching Function ◽  
Operational Mechanism ◽  
Loading Mode ◽  
Automated Method ◽  
Load Function ◽  
Base Address ◽  
Automated Tool

Firmware is software embedded in a device and acts as the most fundamental work of a system. Disassembly is a necessary step to understand the operational mechanism or detect the vulnerabilities of the firmware. When disassembling a firmware, it should first obtain the processor type of running environment and the image base of firmware. In general, the processor type can be obtained by tearing down the device or consulting the product manual. However, at present, there is still no automated tool that can be used to obtain the image base of all types of firmware. In this paper, we focus on firmware in ARM and propose an automated method to determine the image base address. Firstly, by studying the storage rule and loading mode of the function address, we can obtain the function offset and the function address loaded by LDR instruction, respectively. Then, with this information, we propose an algorithm, named Determining image Base by Matching Function Addresses (DBMFA), to determine the image base. The experimental results indicate that the proposed method can successfully determine the image base of firmware which uses LDR instruction to load function address.

scholarly journals Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type Caenorhabditis elegans for the Death Criterion

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5981
Author(s):  
Joan Carles Puchalt ◽  
Pablo E. Layana Castro ◽  
Antonio-José Sánchez-Salmerón
Keyword(s):  
Caenorhabditis Elegans ◽  
Experimental Results ◽  
Artificial Vision ◽  
Wild Type ◽  
Worm Detection ◽  
C Elegans ◽  
Automated Method ◽  
Live Worm ◽  
Lifespan Variability ◽  
Stimulation Of

Nowadays, various artificial vision-based machines automate the lifespan assays of C. elegans. These automated machines present wider variability in results than manual assays because in the latter worms can be poked one by one to determine whether they are alive or not. Lifespan machines normally use a “dead or alive criterion” based on nematode position or pose changes, without poking worms. However, worms barely move on their last days of life, even though they are still alive. Therefore, a long monitoring period is necessary to observe motility in order to guarantee worms are actually dead, or a stimulus to prompt worm movement is required to reduce the lifespan variability measure. Here, a new automated vibrotaxis-based method for lifespan machines is proposed as a solution to prompt a motion response in all worms cultured on standard Petri plates in order to better distinguish between live and dead individuals. This simple automated method allows the stimulation of all animals through the whole plate at the same time and intensity, increasing the experiment throughput. The experimental results exhibited improved live-worm detection using this method, and most live nematodes (>93%) reacted to the vibration stimulus. This method increased machine sensitivity by decreasing results variance by approximately one half (from ±1 individual error per plate to ±0.6) and error in lifespan curve was reduced as well (from 2.6% to 1.2%).

Blending Ensemble of Fine-Tuned Convolutional Neural Networks Applied to Mammary Image Classification

2019 ◽  
Vol 9 (6) ◽  
pp. 1160-1166 ◽  
Author(s):  
Jingyi Zhang ◽  
Shuwan Pan ◽  
Huichao Hong ◽  
Lingke Kong
Keyword(s):  
Human Error ◽  
Poor Performance ◽  
Experimental Results ◽  
Accuracy Requirement ◽  
Automated Method ◽  
Normal Image ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Deep Cnn

Medical images classification is a challenging research topic in the field of computer vision, especially when applied to diagnosis of breast cancer (BC). Nowadays, histopathological image is marked as the gold standard for diagnosing BC. However, such diagnosis is heavily dependent on the clinician's experience, which is extremely time consuming and is subjected to human error even for experienced doctors. To address those problems, this paper implements an automated method for distinguishing the benign from the malignant tumor based on a convolutional neural network (CNN). Traditional deep CNN and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between some key features and image tags. To further meet the high accuracy requirement of diagnosis, according to the characteristics of histopathological images, we propose a novel CNN framework. Firstly, a normal image is augmented to solve the problem about having a limited database. Secondly, we introduce transfer learning to obtain more accurate weight parameters that were pre-trained on the ImageNet. Thirdly, we combine various features extracted by many individual models to obtain comprehensive features. Finally, random forest is introduced to enforce classification. The experimental results show that novel CNN frameworks have better performance compared with individual models, including DenseNet and ResNet. Experimental results are able to prove the effectiveness of our strategy.

scholarly journals Determining the Image Base of Smart Device Firmware for Security Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ruijin Zhu ◽  
Baofeng Zhang ◽  
Yu-an Tan ◽  
Jinmiao Wang ◽  
Yueliang Wan
Keyword(s):  
Security Analysis ◽  
Experimental Results ◽  
Common Method ◽  
Smart Devices ◽  
Smart Device ◽  
Security Issues ◽  
Automated Method ◽  
The Absolute

The authorization mechanism of smart devices is mainly implemented by firmware, yet many smart devices have security issues about their firmware. Limited research has focused on securing the firmware of smart devices, although increasingly more smart devices are used to deal with the very sensitive applications, activities, and data of users. Thus, research on smart device firmware security is of growing importance. Disassembly is a common method for evaluating the security of authorization mechanisms. When disassembling firmware, the processor type of the running environment and the image base of the firmware should first be determined. In general, the processor type can be obtained by tearing down the device or consulting the product manual. However, it is not easy to determine the image base of firmware. Since the processors of many smart devices are ARM architectures, in this paper, we focus on firmware under the ARM architecture and propose an automated method for determining the image base. By studying the storage law of the jump table in the firmware of ARM-based smart devices, we propose an algorithm, named determining the image base by searching jump tables (DBJT), to determine the image base. The experimental results indicate that the proposed method can successfully determine the image base of firmware, which stores the absolute address in the jump table.

scholarly journals SimpylCellCounter: An Automated Solution for Quantifying Cells in Brain Tissue

2020 ◽  
Author(s):  
Aneesh Bal ◽  
Fidel Maureira ◽  
Amy A. Arguello
Keyword(s):  
Brain Tissue ◽  
Absolute Error ◽  
Brain Regions ◽  
Neural Network Classifier ◽  
New Approach ◽  
General Utility ◽  
Automated Method ◽  
Induced Changes ◽  
Automated Tool

ABSTRACTRationale & ObjectiveManual quantification of activated cells can provide valuable information about stimuli-induced changes within brain regions; however, this analysis remains time intensive. Therefore, we created SimpylCellCounter (SCC), an automated method to quantify cells that express Cfos protein, an index of neuronal activity, in brain tissue and benchmarked it against two widely-used methods: OpenColonyFormingUnit (OCFU) and ImageJ Edge Detection Macro (IMJM).MethodsIn Experiment 1, manually-obtained counts were compared to those detected via OCFU, IMJM and SCC. The absolute error in counts (manual versus automated method) was calculated, and error types were categorized as false positives or negatives. In Experiment 2, performance analytics of OCFU, IMJM and SCC were compared. In Experiment 3, SCC performed analysis on images it was not trained on, to assess its general utility.Results & ConclusionsWe found SCC to be highly accurate and efficient in quantifying both cells with circular morphologies and those expressing Cfos. Additionally, SCC utilizes a new approach for counting overlapping cells with a pretrained convolutional neural network classifier. The current study demonstrates that SCC is a novel, automated tool to quantify cells in brain tissue, complementing current, open-sourced quantification methods designed to detect cells in vitro.

Diagnostics of Gold Laser Plasmas

1988 ◽  
Vol 102 ◽  
pp. 357-360
Author(s):  
J.C. Gauthier ◽  
J.P. Geindre ◽  
P. Monier ◽  
C. Chenais-Popovics ◽  
N. Tragin ◽  
...  
Keyword(s):  
Experimental Results ◽  
Pure Gold ◽  
Electronic Temperature ◽  
X Ray ◽  
Laser Plasmas ◽  
Collisional Radiative Model ◽  
Radiative Model

AbstractIn order to achieve a nickel-like X ray laser scheme we need a tool to determine the parameters which characterise the high-Z plasma. The aim of this work is to study gold laser plasmas and to compare experimental results to a collisional-radiative model which describes nickel-like ions. The electronic temperature and density are measured by the emission of an aluminium tracer. They are compared to the predictions of the nickel-like model for pure gold. The results show that the density and temperature can be estimated in a pure gold plasma.

Observation of Phase Contrast Images in STEM and CTEM Modes by Using 100 kV Field Emission Electron Gun

1974 ◽  
Vol 32 ◽  
pp. 390-391
Author(s):  
Y. Harada ◽  
T. Goto ◽  
H. Koike ◽  
T. Someya
Keyword(s):  
Field Emission ◽  
Phase Contrast ◽  
Image Formation ◽  
Fresnel Diffraction ◽  
Experimental Results ◽  
Electron Gun ◽  
Scanning Microscopy ◽  
Emission Electron ◽  
Lattice Images

Since phase contrasts of STEM images, that is, Fresnel diffraction fringes or lattice images, manifest themselves in field emission scanning microscopy, the mechanism for image formation in the STEM mode has been investigated and compared with that in CTEM mode, resulting in the theory of reciprocity. It reveals that contrast in STEM images exhibits the same properties as contrast in CTEM images. However, it appears that the validity of the reciprocity theory, especially on the details of phase contrast, has not yet been fully proven by the experiments. In this work, we shall investigate the phase contrast images obtained in both the STEM and CTEM modes of a field emission microscope (100kV), and evaluate the validity of the reciprocity theory by comparing the experimental results.

Computer Indexing of Electron Diffraction Patterns Including the Effects of Lattice Symmetry

1977 ◽  
Vol 35 ◽  
pp. 22-23
Author(s):  
J. S. Lally ◽  
R. J. Lee
Keyword(s):  
Electron Diffraction ◽  
Zone Axis ◽  
Crystal Thickness ◽  
Double Diffraction ◽  
Automated Method ◽  
Lattice Symmetry ◽  
Intensity Calculations ◽  
Unknown Structure ◽  
Diffraction Patterns ◽  
Orientation Parameters

In the 50 year period since the discovery of electron diffraction from crystals there has been much theoretical effort devoted to the calculation of diffracted intensities as a function of crystal thickness, orientation, and structure. However, in many applications of electron diffraction what is required is a simple identification of an unknown structure when some of the shape and orientation parameters required for intensity calculations are not known. In these circumstances an automated method is needed to solve diffraction patterns obtained near crystal zone axis directions that includes the effects of systematic absences of reflections due to lattice symmetry effects and additional reflections due to double diffraction processes.Two programs have been developed to enable relatively inexperienced microscopists to identify unknown crystals from diffraction patterns. Before indexing any given electron diffraction pattern, a set of possible crystal structures must be selected for comparison against the unknown.

A new dissociation mode for dislocations in silicon

1978 ◽  
Vol 36 (1) ◽  
pp. 346-347
Author(s):  
A. Ourmazd ◽  
G.R. Booker ◽  
C.J. Humphreys
Keyword(s):  
Detailed Examination ◽  
Experimental Results ◽  
Dislocation Network ◽  
Weak Beam ◽  
Phosphorus Doped

A (111) phosphorus-doped Si specimen, thinned to give a TEM foil of thickness ∼ 150nm, contained a dislocation network lying on the (111) plane. The dislocation lines were along the three <211> directions and their total Burgers vectors,ḇt, were of the type , each dislocation being of edge character. TEM examination under proper weak-beam conditions seemed initially to show the standard contrast behaviour for such dislocations, indicating some dislocation segments were undissociated (contrast A), while other segments were dissociated to give two Shockley partials separated by approximately 6nm (contrast B) . A more detailed examination, however, revealed that some segments exhibited a third and anomalous contrast behaviour (contrast C), interpreted here as being due to a new dissociation not previously reported. Experimental results obtained for a dislocation along [211] with for the six <220> type reflections using (g,5g) weak-beam conditions are summarised in the table below, together with the relevant values.

Multislice calculations of RHEED patterns from vicinal Si (001)

1993 ◽  
Vol 51 ◽  
pp. 1000-1001
Author(s):  
Scott Lordi
Keyword(s):  
Misorientation Angle ◽  
Incident Angle ◽  
Incident Beam ◽  
Experimental Results ◽  
Bragg Condition ◽  
Vicinal Surfaces ◽  
Kinematic Theory ◽  
Step Edges

Vicinal Si (001) surfaces are interesting because they are good substrates for the growth of III-V semiconductors. Spots in RHEED patterns from vicinal surfaces are split due to scattering from ordered step arrays and this splitting can be used to determine the misorientation angle, using kinematic arguments. Kinematic theory is generally regarded to be inadequate for the calculation of RHEED intensities; however, only a few dynamical RHEED simulations have been attempted for vicinal surfaces. The multislice formulation of Cowley and Moodie with a recently developed edge patching method was used to calculate RHEED patterns from vicinal Si (001) surfaces. The calculated patterns are qualitatively similar to published experimental results and the positions of the split spots quantitatively agree with kinematic calculations.RHEED patterns were calculated for unreconstructed (bulk terminated) Si (001) surfaces misoriented towards [110] ,with an energy of 15 keV, at an incident angle of 36.63 mrad ([004] bragg condition), and a beam azimuth of [110] (perpendicular to the step edges) and the incident beam pointed down the step staircase.

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